danvk/dygraphs

Interactive visualizations of time series using JavaScript and the HTML canvas tag

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danvk/dygraphs has 3k+ stars and 550+ forks on GitHub, is written in JavaScript, and was last pushed on Jul 29, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 12 minutes ago
Added to GitGenius on September 21st, 2026
Created on November 24th, 2009
Open Issues & Pull Requests: 265 (+0)
GitHub issues: Enabled
Number of forks: 588
Total Stargazers: 3,243 (+0)
Total Subscribers: 108 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 8.1 hours
Mean response time: 584.3 days
90th percentile: 3306.7 days
Tracked items: 18

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Issue Activity (beta)

Open issues: 4
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 2,065 days
Stale 30+ days: 4
Stale 90+ days: 4

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

dygraphs is a JavaScript charting library that produces interactive, zoomable visualizations of time series data using the HTML canvas tag.

The library addresses the need to display and explore time series data directly in the browser without requiring server-side processing or Flash. It renders charts using canvas, enabling smooth interactivity including pan and zoom operations. Users can hover over data points to see values, adjust averaging periods for smoothing, and customize appearance through an extensive set of options. The tool supports error bands around data series and integrates with the Google Visualization API.

The library suits projects that need lightweight, client-side time series visualization without external dependencies. It works well for dashboards, monitoring interfaces, and any application displaying temporal data. The README emphasizes that installation should use the prebuilt tarball from NPM rather than building from the git repository, as the latter fails to build source code on install. For developers seeking alternatives or comparisons, the README does not name competing libraries.

The project maintains a clear development workflow documented in a developer guide, with a build process requiring standard tools like Python and npm. The repository explicitly states that contributions prepared using large language models or AI are not welcome, reflecting a deliberate stance on the nature of code in the project.